Analysis of biological networks is now possible because of novel tools and technologies. To understand how complex cellular systems operate it will be necessary to identify and study these biological networks. Transcription factors can elicit global changes in gene expression. Identification of DNA targets of transcription factors on a genome-wide scale has been impracticable until now. Recent advances in sequencing technology now provide platforms to address previously "unanswerable questions." The purpose of this proposal is to use next generation sequencing approaches to identify genome-wide maps of transcription factor:DNA interactions in Arabidopsis thaliana. In addition, development of novel computational methods for data analysis will be explored. The highly characterized floral transition of Arabidopsis provides an excellent model to establish and explore genome-wide transcription factor mapping. Identification of these transcription factor: DNA targets will provide the basis for further exploration of genes previously unidentified to have a role in flowering, of auto-regulation of transcription, of positive and negative feedback regulation of transcription, of cross talk with other biological processes and of consensus sequences required for binding. Upon establishment of these transcription factor:DNA interaction maps, exploration of natural variation of transcriptional networks will commence. Studies of genome-wide transcription factor maps between natural accessions could provide targets that contribute to phenotypic variation. In conclusion, the goal of this proposal is to identify genome-wide transcription factor DNA interactions. This work will establish a scaffold to study any protein: DNA interaction in any organism with a sequenced genome and ultimately pave the way for identification of transcriptional networks in specific cell types/tissues associated with disease states. Genome-wide maps of protein:DNA interactions as well as advances to methodology used in these types of analyses will provide a framework to explore diseases at the level of transcription. Analysis of changes to these transcriptional networks, in diseased states, will provide the basis for further exploration. [unreadable] [unreadable] [unreadable]